Sensor devices of various types are increasingly common, producing a large amount of information regarding diverse physical variables. Such information is useful for a wide range of applications, such as surveillance, habitat monitoring, target tracking, factory control, structural health monitoring, assisted living, pipeline integrity and tank level monitoring. With recent advances in microelectronics and wireless communication techniques, current sensors are full-fledged computer systems, typically comprising a central processing unit (CPU), main memory, operating system and radio interfaces.
A promising solution for applications based on sensing data is to employ a set of sensors and interconnect them via radio links to compose a wireless sensor network (WSN). Such networks provide a powerful distributed data acquisition system. Sensor nodes act in a collaborative way to perform sensing tasks providing data with scale. The use of wireless communications enables the configuration and reconfiguration of sensors installed in an easy, fast and inexpensive way. Individual sensor readings, however, are subject to environmental noise and inaccuracies that can affect the data quality. In addition, since sensors are typically operated by non-rechargeable batteries, it is essential to optimize sensing and communication tasks in order to extend the operational lifetime of the network. In this context, extracting useful information from the myriad of sensor-collected data in order to meet high-level goals of applications in a timely manner requires a large development effort and presents several research challenges involving data analytics and optimization techniques in order to provide high quality information while dealing with constraints and specific features of WSN. These unique features naturally motivate the involvement of autonomic capabilities in WSNs.
The fundamental characteristic of an autonomic system is self-management, meaning the ability to perform routine tasks such as configuration and maintenance without the intervention of the human system administrator. The system must be continuously self-monitored to be aware of changes in the execution environment, which may require some reconfiguration and optimization of components in order to protect against a suspected faulty or inefficient behavior or to recover from failures. Therefore, an autonomic system must be provided with context aware adaptation capabilities. Context can be defined as any information that can be used to characterize the situation of an entity, where the entity can be a person, place, or object that is considered relevant to the interaction between the user and the entity, including itself and users. In the WSN scenario, context refers to the state of the network (including the devices and the connectivity between them) and of the application.
A need exists for an improved architecture and processes for managing sensor data.